Long-tailed classification.
Early works focus on re-balancing the contribution of each class in the training phase, which can be divided into two strategies: re-sampling the data to balance the class frequency [1]}, [2]}, [3]}, [4]}, [5]}, and re-weighting the loss of classes [6]}, [7]}, [8]}, [9]} to increase the importance of tail classes. Nevertheless, both strategies suffer from under-fitting/over-fitting problem to head/tail classes. Recent studies shows a trend of decoupling long-tailed classification into two separate stages: representation learning and classifier learning. Decouple [10]} learns high-quality representation with natural (instance-balanced) sampling, and achieve strong classification performance by adjusting classifier with balanced learning. [11]} arrives at a similar conclusion by proposing a bilateral-branch network, where one branch is trained with natural sampling and the other uses balanced sampling. The decoupled learning idea is also adopted in [12]}, [13]}, [14]}, [15]}, where different classifier adjustment methods are proposed. There is a line of post-hoc logits adjustment by subtracting the training prior from the predicted logits [12]}, [13]}. [14]} removes the “head” causal effect to get the adjusted logits. DisAlign [15]} modifies the original logits by adding an extra learnable layer to the output layer.
Apart from decoupled learning, [20]} addresses the long-tailed problem by transferring head distributions to tail; distilling balanced student models from imbalanced trained teacher models [21]}, [22]}. Although all the above methods manifest an overall accuracy improvement, as we discussed in Section , they are indeed biased.
